Wellbeing vs competency? Debunking the false dichotomy in medical education
Bibliographic record
Abstract
Numerous studies have shown that medical learners experience poorer wellbeing than their counterparts in the general population. Over the last decade, medical learner wellbeing has become front-of-mind for educators and administrators, which has helped drive systematic improvements in learning and working environments. However, as awareness has grown on the importance of learner wellbeing, a parallel narrative has emerged that questions whether these initiatives are impacting the development of medical competency. In this article, the authors argue that the false dichotomy of wellbeing vs competency stems from a historical medical culture that prized self-sacrifice and "toughness" as markers of competence. There is no doubt that professional growth in medicine requires elements of discomfort and uncertainty. However, the line between productive stress and harm has historically been blurred and pushed by medical training. This culture of "toughness" consequently reinforces a harmful hidden curriculum that dissuades learners from raising appropriate concerns about excessive workloads and mistreatment. However, the evidence is clear that enhanced learner wellbeing promotes competency and patient safety, rather than detracts from it. The authors, therefore, propose a set of actionable steps to support both the personal health and professional development of learners. This includes distinguishing between necessary and unnecessary discomfort, integrating wellbeing into continuous quality improvement, fostering open and safe dialogue between learners and faculty, as well as committing to a cultural shift in medical education that embeds wellbeing into structural systems and policies. Through recognizing wellbeing as an integral part of competency, learners can be supported to become highly skilled, resilient, and compassionate members of the health workforce.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.085 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".